Police Militarization in Minnesota: A Look at LESO Public Information

library(tidyverse)
library(ggplot2)
library(ggthemes)
library(readxl)
library(plotly)
library(lubridate)
library(RColorBrewer)
options(scipen=999) # don't use scientific notation

Data background and prep

According to 10 U.S. Code § 2576a, “the Secretary of Defense may transfer to Federal and State agencies personal property of the Department of Defense, including small arms and ammunition, that the Secretary determines is: (A) suitable for use by the agencies in law enforcement activities, including counterdrug, counterterrorism, and border security activities; and (B) excess to the needs of the Department of Defense.”

Interestingly, section (e) of this law also clearly spells out the requirements for making data about these property transfers open and accessible to the public. It mandates that “[t]he Secretary shall create and maintain a publicly available Internet website that provides information on the controlled property transferred under this section and the recipients of such property.” That website is located here. Analyzing the available data can provide some interesting insights into how military-grade equipment is made available to civilian law enforcement agencies.

mn_equip_transfers <- read_excel("./data/LESO_public_information/DISP_AllStatesAndTerritories_03312020.xlsx", sheet="Minnesota")

names(mn_equip_transfers) <- make.names(names(mn_equip_transfers), unique=TRUE) # Automatically clean up column names

mn_equip_transfers$Ship.Date <- as.Date(mn_equip_transfers$Ship.Date)

mn_equip_transfers <- mn_equip_transfers %>%
  mutate(Ship.Year = year(Ship.Date),
         Ship.Month = month(Ship.Date))

Exploratory analysis

Which Minnesota agencies have had the highest volume of equipment transfers (based on total value of goods acquired)?

top_acquiring_agencies <- mn_equip_transfers %>%
  group_by(Station.Name..LEA.) %>%
  summarise(sum_total_acquisitions = sum(Acquisition.Value)) %>%
  top_n(25) %>%
  select(Station.Name..LEA.)

mn_equip_transfers %>%
  filter(Station.Name..LEA. %in% top_acquiring_agencies$Station.Name..LEA.) %>%
  group_by(Station.Name..LEA., Ship.Year) %>%
  summarise(sum_total_acquisitions = sum(Acquisition.Value)) %>%
ggplot(., aes(x=Station.Name..LEA., y=sum_total_acquisitions, fill=as.factor(Ship.Year))) +
  geom_bar(stat="identity", position="stack", color="#ffffff") +
  geom_text(aes(label=Ship.Year), position = position_stack(vjust = 0.5), size=3) +
  scale_fill_discrete(name = "Acquisition Year") +
  xlab("Law enforcement agency") +
  ylab("Total acquisition value") +
  labs(title="Top 25 Minnesota law enforcement agencies for DoD equipment transfers",
        subtitle="Based on total value of goods acquired") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

What are the most popular items across all agencies in the state (based on the count of items acquired)?

mn_equip_transfers %>%
  group_by(Item.Name) %>%
  summarise(count = sum(Quantity)) %>%
  arrange(desc(count)) %>%
  top_n(25) %>%
ggplot(., aes(x=reorder(Item.Name, -count), y=count)) +
  geom_bar(stat="identity") +
  xlab('Transferred item') +
  ylab('Count acquired') +
  labs(title="Top 25 most popular items transferred from DoD to MN law enforcement agencies",
       subtitle="Based on count of items acquired") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

What are the “big ticket” items over the years?

p <- mn_equip_transfers %>%
  group_by(Ship.Year, Item.Name) %>%
  summarise(sum_total_acquisitions = sum(Acquisition.Value),
            count_of_item = n(),
            acquiring_agencies = paste(unique(Station.Name..LEA.), collapse="<br>")) %>%
  #filter(sum_total_acquisitions > 80000) %>% # only look at items that totaled over $80000 in that year
ggplot(., aes(x = Ship.Year, y=sum_total_acquisitions, fill=Item.Name, 
              text=paste(Item.Name, 
                         '<br>Count: ', count_of_item, 
                         '<br>Total value: ', sum_total_acquisitions,
                         '<br>Acquiring agencies: <br>', acquiring_agencies))) +
  geom_bar(stat="identity", position="stack") +
  xlab('Shipment year') +
  ylab('Total acquisition value') +
  labs(title="Items by year and acquisition value (hover/click/zoom to see details)") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
        legend.position = "none")

ggplotly(p, tooltip="text", width=800, height=600)

Analysis of item justifications

LESO furnishes a dataset that contains all requests made since the beginning of the year. This dataset contains a text field labeled Justification that contains a description of why the agency is requesting the item.

shipments <- read_excel("./data/LESO_public_information/DISP_Shipments_Cancellations_01012020_03312020.xlsx", sheet="SHIPMENTS")

names(shipments) <- make.names(names(shipments), unique=TRUE) # Automatically clean up column names

shipments$Ship.Date <- as.Date(shipments$Date.Shipped)

mn_shipments <- shipments %>%
  filter(State == 'MN')

Let’s look at some surveillance-related items and examine the justifications given by the various law enforcement agencies for requesting these items from the Department of Defense.

Vehicles and robots

mn_shipments %>% select(Station.Name..LEA., Item.Name, Quantity, Justification) %>%
  filter(str_detect(Item.Name, 'VEHICLE|BOT'))

Night vision/thermal imaging technologies

mn_shipments %>% select(Station.Name..LEA., Item.Name, Quantity, Justification) %>%
  filter(str_detect(Item.Name, 'NIGHT|IMAG'))

A few interesting/mundane things

mn_shipments %>% select(Station.Name..LEA., Item.Name, Quantity, Justification) %>%
  filter(str_detect(Item.Name, 'TRANSL|THERMOMETER|PRINTER|SCREWDRIVER|SWEATER|TV|STOOL'))

The full dataset

In all, Minnesota agencies have made 602 item requests since the start of the year. Here is the full set of items and their justifications:

mn_shipments %>% select(Station.Name..LEA., Item.Name, Quantity, Justification)

References

Data

• https://www.dla.mil/DispositionServices/Offers/Reutilization/LawEnforcement/PublicInformation/

• https://www.dla.mil/DispositionServices/DDSR/quicklinks/demilcodes/